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This issue features 16 research papers contributing novel approaches across diverse domains: smart farming system for sustainable agriculture in Senegal, wearable technology for analyzing sleep patterns, machine learning resume screening, enhanced IoT security framework, emotion analysis framework for facial expressions, pervious concrete for urban drainage in South Africa, hybrid deep learning model for network anomaly detection, historical perspective on machine translation evolution for low-resource languages, augmented reality for railroad maintenance training, culturally-tailored mobile app for Native American diabetes management, smart agent architecture for direct load control, Diffuse Kalman Filter for autonomous vehicle state estimation, stability assessment of explainable AI algorithms, evaluation of Macaca fascicularis heart rate data for privacy methodologies, automated GSM signal strength and meteorological measurement device, and mathematical model for five-phase permanent magnet generator in wind turbines.
Editorial
Front Cover
Adv. Sci. Technol. Eng. Syst. J. 10(2), (2025);
Editorial Board
Adv. Sci. Technol. Eng. Syst. J. 10(2), (2025);
Editorial
Adv. Sci. Technol. Eng. Syst. J. 10(2), (2025);
Table of Contents
Adv. Sci. Technol. Eng. Syst. J. 10(2), (2025);
Articles
Utilization of Generative Artificial Intelligence to Improve Students’ Visual Literacy Skills
Andi Kristanto , Utari Dewi, Dina Fitria Murad, Yumiati, Santi Dewiki, Tiara Sevi Nurmanita
Adv. Sci. Technol. Eng. Syst. J. 10(3), 1-8 (2025);
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This study aims to examine the impact of Gen AI utilization on students’ visual literacy skills using a quantitative approach and data instruments in the form of post-test scores of the control class and experimental class which are analyzed to measure the effectiveness of GEN AI in improving students’ visual literacy skills at four universities. Data processing is carried out through three stages of testing, namely the normality test using Shapiro-Wilk, the homogeneity test of variance with Levene, and the independent sample test to compare the results between two groups of students with questionnaire instruments, observation guidelines, and interviews. The data is processed using a t-test to determine the average difference between groups, especially between the control class that applies conventional learning methods and the experimental class that utilizes GEN AI. The results of the needs analysis show that around 65% of students still have low visual literacy skills based on the quality of graphic media products produced by students. These findings indicate an urgent need to improve visual literacy skills among students, especially in the context of utilizing modern technology such as GEN AI. This research makes a significant contribution to the development of a curriculum that is more responsive to the needs of visual literacy in the digital era, as well as encouraging the integration of technology in the learning process and is expected to be a reference for the development of more innovative and effective learning strategies to improve students’ visual literacy skills in higher education.